Among the strategies utilized to promote fuel efficiency and reduce emissions in vehicles is the use of optimized acceleration curves. Typically, optimized acceleration curves are generated through extensive Research & Development by vehicle manufactures during the multi-year development process for a new platform. The results are typically integrated into the electronic control unit (ECU) that monitors the accelerator pedal position (APP) and otherwise translates the observed APP into signals that operate various actuators or switches that control the fuel flow or electrical power provided to the engine or motor.
In many cases, especially when starting from a dead stop or at low speeds, optimizing for fuel efficiency and reduced emissions translates into light acceleration at low RPMs. Despite this understanding in the industry, new platforms typically do not implement fully optimized acceleration curves, which results in reduced fuel efficiency and increased emissions.
The factors driving implementation of less than optimal acceleration curves principally have to do with human factors. First, a vehicle perceived as noticeably “slow” will not be as easily saleable to the entire pool of potential buyers. Second, any gains from further moderating the acceleration curve may prove illusory in real world driving as vehicle operators often consciously and unconsciously increase acceleration in order to compensate for the perceived “slowness” of the platform—a phenomenon especially well understood by commercial fleet operators, whose vehicles are driven by non-owners.
A third factor involves the vehicle itself. In general, a vehicle performs best from nearly every standpoint in the months and years closest to its first purchase; however, over time, drivetrain wear and other factors related to use and age—such as gas escaping around a cylinder head—cause performance to slowly degrade. As a result, an acceleration curve optimized for the close tolerances of a new vehicle may, over time, increasingly prove less optimal as the drivetrain and related components of the vehicle deviate from factory specification, either through wear or replacement.
A fourth factor involves a purely engineering concern that setting the optimization level too high may, indeed, risk running afoul of slight deviations in manufacturing tolerances in parts from a component vendor or even between otherwise identical parts from different vendors.
A fifth factor also involves the manufacturing process. Development of a new vehicle platform is a multi-year process. Optimization analysis and implementation occurs at several points in that process, but typically not as the very last step prior to manufacture. Any changes to the platform's performance between the last optimization and the commencement of manufacturing may generate deviations from the “last known” good optimization developed by the manufacturer.
A sixth distinct factor involves the new vehicle testing regime. Manufacturers of new vehicles optimize performance for EPA mileage reporting, with testing typically occurring on a dynamometer in a controlled laboratory environment. These tests are far from “real world” driving. As may be expected, optimizing an acceleration curve to maximize performance in a controlled environment abstracted from everyday road conditions and environmental factors is likely to produce less-than-optimal performance in real-world driving.
In light of these and other factors, manufacturers typically implement less than optimal acceleration curves from a fuel efficiency and emissions standpoint in both human-operated and autonomous vehicles. For example, several approaches have been employed to optimize acceleration for purposes of fuel efficiency and/or emissions reduction. These include, for example:
U.S. Patent Publication No. 2013/0275013 to Thejovardhana, and assigned to Automatic Labs, Inc., relates to a system for collecting data related to fuel consumption and driving behavioral data from a monitored vehicle and providing (typically auditory or visual) feedback information to the operator in order to “coach” better driving habits. However, this approach is deficient because it functions purely to train a willing operator to mitigate his or her acceleration demands, but does not improve the underlying efficiency of the acceleration curve at a system level or provide a solution for vehicles operated by non-owners that may otherwise not be inclined to drive more slowly.
U.S. Pat. No. 7,603,228 to Coughlin, and assigned to Ford Global Technologies, LLC, relates to a haptic apparatus for providing feedback to the operator. Rather than further optimizing the underlying acceleration curve in a manner that is invisible to the vehicle operator, Coughlin focuses on an apparatus attached to the accelerator pedal that applies pressure to counter the downward pressure (i.e., acceleration demand) by the operator. However, in a real world environment, these types of systems would prove to be counterproductive because operators would unconsciously (or even deliberately) increase the downward pressure on the pedal to achieve the desired speed. Thus, Coughlin is also deficient.
As set forth above, there is a need for an automatic system, method, and apparatus configuration to optimize the acceleration curve of a vehicle in a manner that is transparent and seamless, without requiring operator training or behavioral modification.